The present invention relates generally to signal processing, and more specifically to an adaptive signal processing system and method for reducing interference in a received signal.
There are many instances where it is desirable to have a sensor capable of receiving an information signal from a particular signal source where the environment includes sources of interference signals at locations different from that of the signal source. One such instance is the use of microphones to record a particular party""s speech in a room where there are other parties speaking simultaneously, causing interference in the received signals.
If one knows the exact characteristics of the interference, one can use a fixed-weight filter to suppress it. But it is often difficult to predict the exact characteristics of the interference because they may vary according to changes in the interference sources, the background noise, acoustic environment, orientation of the sensor with respect to the signal source, the transmission paths from the signal source to the sensor, and many other factors. Therefore, in order to suppress such interference, an adaptive system that can change its own parameters in response to a changing environment is needed.
An adaptive filter is an adaptive system that can change its own filtering characteristics in order to produce a desired response. Typically, the filter weights defining the. characteristics of an adaptive filter are continuously updated so that the difference between a signal representing a desired response and an output signal of the adaptive filter is minimized.
The use of adaptive filters for reducing interference in a received signal has been known in the art as adaptive noise cancelling. It is based on the idea of cancelling a noise component of a received signal from the direction of a signal source by sampling the noise independently of the source signal and modifying the sampled noise to approximate the noise component in the received signal using an adaptive filter. For a seminal article on adaptive noise cancelling, see B. Widrow et al., Adaptive Noise Cancelling: Principles and Applications, Proc. IEEE 63:1692-1716, 1975.
A basic configuration for adaptive noise cancelling has a primary input received by a microphone directed to a desired signal source and a reference input received independently by another microphone directed to a noise source. The primary input contains both a source signal component originating from the signal source and a noise component originating from the noise source. The noise component is different from the reference input representing the noise source itself because the noise signal must travel from the noise source to the signal source in order to be included as the noise component.
The noise component, however, is likely to have some correlation with the reference input because both of them originate from the same noise source. Thus, a filter can be used to filter the reference input to generate a cancelling signal approximating the noise component. The adaptive filter does this dynamically by generating an output signal which is the difference between the primary input and the cancelling signal, and by adjusting its filter weights to minimize the mean-square value of the output signal. When the filter weights settle, the output signal effectively replicates the source signal substantially free of the noise component because the cancelling signal closely tracks the noise component.
Adaptive noise cancelling can be combined with beamforming, a known technique of using an array of sensors to improve reception of signals coming from a specific direction. A beamformer is a spatial filter that generates a single channel from multiple channels received through multiple sensors by filtering the individual multiple channels and combining them in such a way as to extract signals coming from a specific direction. Thus, a beamformer can change the direction of receiving sensitivity without physically moving the array of sensors. For details on beamforming, see B. D. Van Veen and K. M. Buckley, Beamforming: Versatile Approach to Spatial Filtering, IEEE ASSP Mag. 5(2), 4-24.
Since the beamformer can effectively be pointed in many directions without physically moving its sensors, the beamformer can be combined with adaptive noise cancelling to form an adaptive beamformer that can suppress specific directional interference rather than general background noise. The beamformer can provide the primary input by spatially filtering input signals from an array of sensors so that its output represents a signal received in the direction of a signal source. Similarly, the beamformer can provide the reference input by spatially filtering the sensor signals so that the output represents a signal received in the direction of interference sources. For a seminal article on adaptive beamformers, see L. J. Griffiths and C. W. Jim, An Alternative Approach to Linearly Constrained Adaptive Beamforming, IEEE Trans. Ant. Prop. AP-30:27-34, 1982.
One problem with a conventional adaptive beamformer is that its output characteristics change depending on input frequencies and sensor directions with respect to interference sources. This is due to the sensitivity of a beamformer to different input frequencies and sensor directions. A uniform output behavior of a system over all input frequencies of interest and over all sensor directions is clearly desirable in a directional microphone system where faithful reproduction of a sound signal is required regardless of where the microphones are located.
Another problem with adaptive beamforming is xe2x80x9csignal leakagexe2x80x9d. Adaptive noise cancelling is based on an assumption that the reference input representing noise sources is uncorrelated with the source signal component in the primary input, meaning that the reference input should not contain the source signal. But this xe2x80x9csignal freexe2x80x9d reference input assumption is violated in any real environment. Any mismatch in the microphones (amplitude or phase) or their related analog front end, any reverberation caused by the surroundings or a mechanical structure, and even any mechanical coupling in the physical microphone structure will likely cause xe2x80x9csignal leakagexe2x80x9d from the signal source into the reference input. If there is any correlation between the reference input and the source signal component in the primary input, the adaptation process by the adaptive filter causes cancellation of the source signal component, resulting in distortion and degradation in performance.
It is also important to confine the adaptation process to the case where there is at least some directional interference to be eliminated. Since nondirectional noise, such as wind noise or vibration noise induced by the mechanical structure of the system, is typically uncorrelated with the noise component of the received signal, the adaptive filter cannot generate a cancelling signal approximating the noise component.
Prior art suggests inhibiting the adaptation process of an adaptive filter when the signal-to-noise ratio (SNR) is high based on the observation that a strong source signal tends to leak into the reference input. For example, U.S. Pat. No. 4,956,867 describes the use of cross-correlation between two sensors to inhibit the adaptation process when the SNR is high.
But the prior art approach fails to consider the effect of directional interference because the SNR-based approach considers only nondirectional noise. Since nondirectional. noise is not correlated to the noise component of the received signal, the adaptation process searches in vain for new filter weights, which often results in cancelling the source signal component of the received signal.
The prior art approach also fails to consider signal leakage when the source signal is of a narrow bandwidth. In a directional microphone application, the source signal often contains a narrow band signal, such as speech signal, with its power spectral density concentrated in a narrow frequency range. When signal leakage occurs due to a strong narrow band signal, the prior art approach may not inhibit the adaptation process because the overall signal strength of such narrow band signal may not high enough. The source signal component of the received signal is cancelled as a result, and if the source signal is a speech signal, degradation in speech intelligibility occurs.
Therefore, there exists a need for an adaptive system that can suppress directional interference in a received signal with a uniform frequency behavior over a wide angular distribution of interference sources.
Accordingly, it is an object of the present invention to suppress interference in a received signal using an adaptive filter for processing inputs from an array of sensors.
Another object of the invention is to limit the adaptation process of such adaptive filter to the case where there is at least some directional interference to be eliminated.
A further object of the invention is to control the adaptation process to prevent signal leakage for narrow band signals.
Another object is to produce an output with a uniform frequency behavior in all directions from the sensor array.
These and other objects are achieved in accordance with the present invention, which uses a system for processing digital data representing signals received from an array of sensors. The system includes a main channel matrix unit for generating a main channel representing signals received in the direction of a signal source where the main channel has a source signal component and an interference signal component. The system includes a reference channel matrix unit for generating at least one reference channel where each reference channel represents signals received in directions other than that of the signal source. The system uses adaptive filters for generating cancelling signals approximating the interference signal component of the main channel and a difference unit for generating a digital output signal by subtracting the cancelling signals from the main channel. Each adaptive filter has weight updating means for finding new filter weights based on the output signal. The system includes weight constraining means for truncating the new filter weight values to predetermined threshold values when each of the new filter weight value exceeds the corresponding threshold value.
The system may further include at least one decolorizing filter for generating a flat-frequency reference channel. The system may further include inhibiting means for estimating the power of the main channel and the power of the reference channels and for generating an inhibit signal to the weight updating means based on normalized power difference between the main channel and the reference channels.
The system produces an output substantially free of directional interference with a uniform frequency behavior in all directions from the system.
The objects are also achieved in accordance with the present invention using a method, which can readily be implemented in a program controlling a commercially available DSP processor.